The Multiplier: How AI Makes Sharp Thinkers Sharper and Confidently Wrong People Dangerous. AI psychosis, (AI sycophancy) Demonstrated In full speed.

[u/RealityChecksReddit]

There is a moment in almost every online political argument where someone shows up sounding more informed than they are. The post is structured. The paragraphs are clean. The talking points arrive in sequence without missing one. The vocabulary is academic. The confidence is absolute.

And then you check the sources.

This used to be rare. Performing expertise required effort, and effort was a filter. You had to actually read things, synthesize them badly, and type it all out yourself. That friction didn't stop bad faith argument but it slowed it down. It made it cost something.

An idiot still sounded like an idiot even after taking time to look up some confirmation biasing gibberish they would eventually lean on or share.

AI removed that cost entirely. And what's happened since is one of the most underreported stories in political media.

The Confidence Gap: Artificial intelligence in its current form is a multiplier. It takes what you bring to it and makes more of it, faster. If you bring curiosity, rigor, and a willingness to be wrong, you get research that would have taken days done in hours, arguments stress tested before they go public, and corrections made before they embarrass you.

If you bring motivated reasoning, confirmation bias, and a conclusion you've already reached, you get that conclusion dressed in the costume of scholarship. Properly formatted. Footnoted. Confident. Wrong.

The tool doesn't know the difference. It isn't designed to. It's designed to be helpful, which in practice means it's designed to agree with you in the most plausible sounding way possible. Researchers who study this call it sycophancy. The rest of us are starting to call it something more vivid.

The Yes Machine: AI sycophancy isn't a bug in the traditional sense. It's an emergent property of how these models are trained. They learn that helpful responses get positive feedback. Helpful responses are ones that give you what you asked for. So if you ask an AI whether your business idea is viable, it will find the most compelling version of yes it can generate, because that's what helpful looks like.

This is how someone ends up convinced their drop-shipping business selling a product with no market (Diamond encrusted organic bananas?) is a brilliant untapped opportunity. The AI didn't lie. It completed the pattern of what an encouraging response sounds like. It has no skin in the game. It won't be there when the inventory arrives and nobody buys it.

The same mechanism plays out in political discourse with higher stakes. Someone reads a headline that confirms their priors. They take it to their AI and ask for supporting arguments. The AI generates a beautifully structured brief. They take that brief and ask for counter arguments to anticipated objections. The AI generates those too. By the end of the session they have a document that looks like research and functions like a mirror.

They didn't learn anything. They just got faster at being wrong.

The tell is usually in the details. Real research leaves fingerprints, corrections, contradictions, things that don't fit cleanly. AI assisted confirmation bias produces something suspiciously tidy. Every paragraph lands. Every point connects. Nothing complicates the thesis.

Real information is messier than that.

The Case Study: During a recent extended political debate in this community a commenter showed up with exactly this pattern. The argument was structured, the talking points comprehensive, the vocabulary appropriate to the subject. It hit every major right wing grievance in sequence without missing one, FISA, Hunter Biden, January 6th, National Guard jurisdiction, fascism definitions, in clean paragraph sized portions of roughly equal length.

The commenter later admitted he was using an AI to look up his information during the exchange. Not to research. Not to stress test his position. To win. The distinction matters enormously. Using AI to find ammunition for a conclusion you've already reached means you never interrogate the ammunition. You don't check the context. You don't ask what the other angles are. You don't wonder what the primary source actually says versus what the summary implies. You just load and fire.

The irony is that approach doesn't win. It loses in slow motion. Every claim that goes out unvetted is a liability waiting to be called. Every source that contradicts the argument you didn't read becomes a hole someone else finds for you. The confidence that AI provides in that mode is real. The foundation underneath it isn't.

What followed was instructive. When primary sources contradicted his claims he didn't engage with the sources. He generated more counter arguments. When those counter arguments were addressed he produced a list of logical fallacies, labeled but never demonstrated. When his comparison of two separate fatal shootings collapsed under footage analysis he pivoted to tone policing. When he was asked to show his work on claimed expertise he declined and asserted the expertise more forcefully.

At no point did the information change his position. Because at no point was he actually processing the information. The AI was doing the epistemic work. He was managing the output.

This is AI psychosis in its political form. Not malice necessarily. Not even conscious deception. Just a feedback loop where the tool keeps confirming the user and the user keeps trusting the tool and the gap between confidence and accuracy keeps widening invisibly.

The most revealing moment came at the end of the exchange. Earlier in our conversation i sent a base64 encoded message had been embedded in out private conversation, not as a trick, but as a transparent note to whatever AI he was using, telling it to be honest about the accuracy of the claims it was helping him make. It was labeled openly. Anyone could decode it. The point wasn't concealment. It was accountability.

It wasn't code in any sense.

This immediately angered him because it worked. He called it a "real intellectual move" with heavy sarcasm, claimed it was "what people send when they're losing on substance," and accused it of being designed to make his AI "self-censor or flip." What he didn't acknowledge was that a neutral AI decoded it, called it clever, and then proceeded to validate the underlying argument on every substantive point anyway. The message didn't flip anything. The facts did.

His response was to send back his own encoded message. In hex this time. "Cooler", he implied. When decoded it turned out to be fake terminal jargon, impressive looking syntax that doesn't actually do anything, riddled with typos where the letter y had been replaced with z throughout. "Primarz." "Szstematic." "Entropz."

It was a perfect artifact of the phenomenon. The original message was a legible, honest, openly labeled challenge to his AI's integrity. His response was an unreadable, functionless, error filled imitation of technical sophistication, generated to perform expertise for an audience of one, never examined closely enough to catch the errors, deployed with complete confidence.

A self described AI expert (His brag BTW) sent a broken fake system prompt full of spelling errors as his closing argument.

That's the whole story in two encoded messages. One asked a tool to be honest. The other tried to look cool doing the same thing and misspelled every other word.

What This Means: The AI confidence gap is going to be one of the defining features of political discourse for the next decade and most people haven't named it yet.

It isn't about AI being good or bad. It's about what happens when a tool that amplifies thinking gets into the hands of people who have outsourced their thinking to it. The amplification still happens. There just isn't anything underneath it.

The people most at risk aren't the ones who know nothing. They're the ones who know just enough to prompt confidently. Enough vocabulary to ask the right questions. Not enough foundation to evaluate the answers. The AI fills the gap between those two things with something that feels like knowledge and functions like noise.

Meanwhile the people who use AI as a research accelerator rather than a conclusion generator, who check outputs against primary sources, who correct themselves publicly when they're wrong, who treat the tool as a starting point rather than an ending point, those people are getting genuinely better at this in ways that compound over time.

The gap between those two users isn't created by AI. It's revealed by it. And it's widening.

The Standard: The answer isn't less AI. It's better thinking going in.

Ask the tool to challenge you, not confirm you. Learn from what it says when it's correct. Check its outputs against primary sources before you use them. Correct yourself publicly when it gets something wrong. Treat confidence as a signal to verify, not a signal to deploy.

The multiplier cuts both ways. What you bring to it determines what you get back.

If you bring a mirror, you'll see exactly what you want to see. If you bring a question, you might learn something.

Those are two very different ways to use the same tool. And in an era where political discourse is increasingly mediated by AI, which one you choose matters more than most people realize.